Recensement

Dataviz sur les données du recensement 2019 avec recoupement sur les logements

!pip install pandas seaborn dataprep
Requirement already satisfied: pandas in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (1.4.2)
Requirement already satisfied: seaborn in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.11.2)
Requirement already satisfied: dataprep in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (0.4.3)
Requirement already satisfied: numpy>=1.18.5 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from pandas) (1.22.3)
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Requirement already satisfied: cffi>=1.0.1 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (1.15.0)
Requirement already satisfied: soupsieve>1.2 in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from beautifulsoup4->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.3.2.post1)
Requirement already satisfied: webencodings in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from bleach->nbconvert>=5->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (0.5.1)
Requirement already satisfied: pycparser in /opt/hostedtoolcache/Python/3.8.12/x64/lib/python3.8/site-packages (from cffi>=1.0.1->argon2-cffi-bindings->argon2-cffi->notebook>=4.4.1->widgetsnbextension~=3.6.0->ipywidgets<8.0,>=7.5->dataprep) (2.21)
WARNING: You are using pip version 22.0.4; however, version 22.1 is available.
You should consider upgrading via the '/opt/hostedtoolcache/Python/3.8.12/x64/bin/python -m pip install --upgrade pip' command.

!wget "https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_individus.csv
--2022-05-13 04:04:05--  https://data.gouv.nc/explore/dataset/rp-2019-indv-psud/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_individus.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ] 100.67K   258KB/s               
        data/recens     [  <=>               ] 228.64K   298KB/s               
       data/recense     [   <=>              ] 292.62K   259KB/s               
      data/recensem     [    <=>             ] 404.60K   265KB/s               
     data/recenseme     [     <=>            ] 500.57K   259KB/s               
    data/recensemen     [      <=>           ] 612.55K   260KB/s               
   data/recensement     [       <=>          ] 668.55K   239KB/s               
  data/recensement_     [        <=>         ] 764.55K   234KB/s               
 data/recensement_i     [         <=>        ] 860.55K   230KB/s               
data/recensement_in     [          <=>       ] 924.55K   220KB/s               
ata/recensement_ind     [           <=>      ]   1021K   220KB/s               
ta/recensement_indi     [            <=>     ]   1.09M   213KB/s               
a/recensement_indiv     [             <=>    ]   1.18M   214KB/s               
/recensement_indivi     [              <=>   ]   1.25M   207KB/s               
recensement_individ     [               <=>  ]   1.34M   208KB/s               
ecensement_individu     [                <=> ]   1.43M   208KB/s               
censement_individus     [                 <=>]   1.50M   207KB/s               
ensement_individus.     [                <=> ]   1.59M   210KB/s               
nsement_individus.c     [               <=>  ]   1.68M   211KB/s               
sement_individus.cs     [              <=>   ]   1.78M   210KB/s               
ement_individus.csv     [             <=>    ]   1.84M   203KB/s               
ment_individus.csv      [            <=>     ]   1.93M   207KB/s               
ent_individus.csv       [           <=>      ]   2.03M   212KB/s               
nt_individus.csv        [          <=>       ]   2.09M   208KB/s               
t_individus.csv         [         <=>        ]   2.18M   209KB/s               
_individus.csv          [        <=>         ]   2.28M   209KB/s               
individus.csv           [       <=>          ]   2.34M   213KB/s               
ndividus.csv            [      <=>           ]   2.43M   216KB/s               
dividus.csv             [     <=>            ]   2.53M   220KB/s               
ividus.csv              [    <=>             ]   2.62M   227KB/s               
vidus.csv               [   <=>              ]   2.68M   226KB/s               
idus.csv                [  <=>               ]   2.78M   232KB/s               
dus.csv                 [ <=>                ]   2.87M   234KB/s               
us.csv                  [<=>                 ]   2.93M   239KB/s               
s.csv                   [ <=>                ]   3.03M   236KB/s               
.csv                    [  <=>               ]   3.12M   240KB/s               
csv                     [   <=>              ]   3.18M   241KB/s               
sv                      [    <=>             ]   3.28M   240KB/s               
v                       [     <=>            ]   3.37M   240KB/s               
                        [      <=>           ]   3.43M   235KB/s               
                  d     [       <=>          ]   3.53M   240KB/s               
                 da     [        <=>         ]   3.62M   237KB/s               
                dat     [         <=>        ]   3.68M   232KB/s               
               data     [          <=>       ]   3.78M   233KB/s               
              data/     [           <=>      ]   3.87M   232KB/s               
             data/r     [            <=>     ]   3.93M   224KB/s               
            data/re     [             <=>    ]   4.03M   226KB/s               
           data/rec     [              <=>   ]   4.12M   223KB/s               
          data/rece     [               <=>  ]   4.18M   219KB/s               
         data/recen     [                <=> ]   4.28M   217KB/s               
        data/recens     [                 <=>]   4.37M   221KB/s               
       data/recense     [                <=> ]   4.43M   218KB/s               
      data/recensem     [               <=>  ]   4.53M   218KB/s               
     data/recenseme     [              <=>   ]   4.62M   222KB/s               
    data/recensemen     [             <=>    ]   4.71M   227KB/s               
   data/recensement     [            <=>     ]   4.78M   223KB/s               
  data/recensement_     [           <=>      ]   4.87M   224KB/s               
 data/recensement_i     [          <=>       ]   4.96M   224KB/s               
data/recensement_in     [         <=>        ]   5.03M   222KB/s               
ata/recensement_ind     [        <=>         ]   5.12M   227KB/s               
ta/recensement_indi     [       <=>          ]   5.21M   227KB/s               
a/recensement_indiv     [      <=>           ]   5.28M   224KB/s               
/recensement_indivi     [     <=>            ]   5.37M   229KB/s               
recensement_individ     [    <=>             ]   5.46M   231KB/s               
ecensement_individu     [   <=>              ]   5.53M   228KB/s               
censement_individus     [  <=>               ]   5.62M   236KB/s               
ensement_individus.     [ <=>                ]   5.71M   239KB/s               
nsement_individus.c     [<=>                 ]   5.78M   237KB/s               
sement_individus.cs     [ <=>                ]   5.87M   243KB/s               
ement_individus.csv     [  <=>               ]   5.96M   244KB/s               
ment_individus.csv      [   <=>              ]   6.06M   246KB/s               
ent_individus.csv       [    <=>             ]   6.12M   246KB/s               
nt_individus.csv        [     <=>            ]   6.21M   245KB/s               
t_individus.csv         [      <=>           ]   6.31M   226KB/s               
_individus.csv          [       <=>          ]   6.37M   219KB/s               
individus.csv           [        <=>         ]   6.46M   217KB/s               
ndividus.csv            [         <=>        ]   6.56M   224KB/s               
dividus.csv             [          <=>       ]   6.65M   224KB/s               
ividus.csv              [           <=>      ]   6.71M   218KB/s               
vidus.csv               [            <=>     ]   6.81M   217KB/s               
idus.csv                [             <=>    ]   6.90M   214KB/s               
dus.csv                 [              <=>   ]   6.96M   212KB/s               
us.csv                  [               <=>  ]   7.06M   210KB/s               
s.csv                   [                <=> ]   7.15M   207KB/s               
.csv                    [                 <=>]   7.21M   205KB/s               
csv                     [                <=> ]   7.31M   203KB/s               
sv                      [               <=>  ]   7.40M   200KB/s               
v                       [              <=>   ]   7.50M   197KB/s               
                        [             <=>    ]   7.56M   194KB/s               
                  d     [            <=>     ]   7.65M   191KB/s               
                 da     [           <=>      ]   7.75M   207KB/s               
                dat     [          <=>       ]   7.81M   207KB/s               
               data     [         <=>        ]   7.90M   213KB/s               
              data/     [        <=>         ]   8.00M   210KB/s               
             data/r     [       <=>          ]   8.06M   215KB/s               
            data/re     [      <=>           ]   8.15M   212KB/s               
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          data/rece     [    <=>             ]   8.34M   216KB/s               
         data/recen     [   <=>              ]   8.40M   211KB/s               
        data/recens     [  <=>               ]   8.50M   219KB/s               
       data/recense     [ <=>                ]   8.59M   222KB/s               
      data/recensem     [<=>                 ]   8.65M   224KB/s               
     data/recenseme     [ <=>                ]   8.75M   227KB/s               
    data/recensemen     [  <=>               ]   8.84M   231KB/s               
   data/recensement     [   <=>              ]   8.93M   238KB/s               
  data/recensement_     [    <=>             ]   9.00M   235KB/s               
 data/recensement_i     [     <=>            ]   9.09M   237KB/s               
data/recensement_in     [      <=>           ]   9.18M   240KB/s               
ata/recensement_ind     [       <=>          ]   9.25M   244KB/s               
ta/recensement_indi     [        <=>         ]   9.34M   249KB/s               
a/recensement_indiv     [         <=>        ]   9.43M   251KB/s               
/recensement_indivi     [          <=>       ]   9.50M   250KB/s               
recensement_individ     [           <=>      ]   9.59M   251KB/s               
ecensement_individu     [            <=>     ]   9.68M   253KB/s               
censement_individus     [             <=>    ]   9.78M   257KB/s               
ensement_individus.     [              <=>   ]   9.84M   252KB/s               
nsement_individus.c     [               <=>  ]   9.93M   254KB/s               
sement_individus.cs     [                <=> ]  10.03M   254KB/s               
ement_individus.csv     [                 <=>]  10.09M   256KB/s               
ment_individus.csv      [                <=> ]  10.18M   253KB/s               
ent_individus.csv       [               <=>  ]  10.28M   249KB/s               
nt_individus.csv        [              <=>   ]  10.31M   246KB/s               
t_individus.csv         [             <=>    ]  10.34M   234KB/s               
_individus.csv          [            <=>     ]  10.43M   226KB/s               
individus.csv           [           <=>      ]  10.46M   219KB/s               
ndividus.csv            [          <=>       ]  10.53M   218KB/s               
dividus.csv             [         <=>        ]  10.56M   212KB/s               
ividus.csv              [        <=>         ]  10.59M   205KB/s               
vidus.csv               [       <=>          ]  10.65M   206KB/s               
idus.csv                [      <=>           ]  10.68M   196KB/s               
dus.csv                 [     <=>            ]  10.71M   185KB/s               
us.csv                  [    <=>             ]  10.78M   185KB/s               
s.csv                   [   <=>              ]  10.87M   173KB/s               
.csv                    [  <=>               ]  10.90M   165KB/s               
csv                     [ <=>                ]  10.93M   165KB/s               
sv                      [<=>                 ]  11.00M   162KB/s               
v                       [ <=>                ]  11.03M   151KB/s               
                        [  <=>               ]  11.12M   142KB/s               
                  d     [   <=>              ]  11.15M   136KB/s               
                 da     [    <=>             ]  11.18M   135KB/s               
                dat     [     <=>            ]  11.25M   140KB/s               
               data     [      <=>           ]  11.28M   134KB/s               
              data/     [       <=>          ]  11.31M   134KB/s               
             data/r     [        <=>         ]  11.37M   131KB/s               
            data/re     [         <=>        ]  11.40M   132KB/s               
           data/rec     [          <=>       ]  11.43M   131KB/s               
          data/rece     [           <=>      ]  11.50M   130KB/s               
         data/recen     [            <=>     ]  11.53M   126KB/s               
        data/recens     [             <=>    ]  11.56M   128KB/s               
       data/recense     [              <=>   ]  11.62M   125KB/s               
      data/recensem     [               <=>  ]  11.65M   121KB/s               
     data/recenseme     [                <=> ]  11.68M   114KB/s               
    data/recensemen     [                 <=>]  11.75M   119KB/s               
   data/recensement     [                <=> ]  11.78M   114KB/s               
  data/recensement_     [               <=>  ]  11.81M   107KB/s               
 data/recensement_i     [              <=>   ]  11.87M   106KB/s               
data/recensement_in     [             <=>    ]  11.90M   103KB/s               
ata/recensement_ind     [            <=>     ]  11.96M   104KB/s               
ta/recensement_indi     [           <=>      ]  12.00M   102KB/s               
a/recensement_indiv     [          <=>       ]  12.03M  95.0KB/s               
/recensement_indivi     [         <=>        ]  12.03M  89.9KB/s               
recensement_individ     [        <=>         ]  12.12M  90.6KB/s               
ecensement_individu     [       <=>          ]  12.15M  89.0KB/s               
censement_individus     [      <=>           ]  12.21M  89.6KB/s               
ensement_individus.     [     <=>            ]  12.25M  90.8KB/s               
nsement_individus.c     [    <=>             ]  12.28M  87.6KB/s               
sement_individus.cs     [   <=>              ]  12.34M  91.9KB/s               
ement_individus.csv     [  <=>               ]  12.37M  91.5KB/s               
ment_individus.csv      [ <=>                ]  12.40M  90.8KB/s               
ent_individus.csv       [<=>                 ]  12.46M  94.8KB/s               
nt_individus.csv        [ <=>                ]  12.50M  98.1KB/s               
t_individus.csv         [  <=>               ]  12.56M  98.9KB/s               
_individus.csv          [   <=>              ]  12.59M   100KB/s               
individus.csv           [    <=>             ]  12.62M   101KB/s               
ndividus.csv            [     <=>            ]  12.68M   103KB/s               
dividus.csv             [      <=>           ]  12.71M   106KB/s               
ividus.csv              [       <=>          ]  12.75M   107KB/s               
vidus.csv               [        <=>         ]  12.81M   108KB/s               
idus.csv                [         <=>        ]  12.84M   110KB/s               
dus.csv                 [          <=>       ]  12.87M   122KB/s               
us.csv                  [           <=>      ]  12.93M   126KB/s               
s.csv                   [            <=>     ]  12.96M   129KB/s               
.csv                    [             <=>    ]  13.00M   131KB/s               
csv                     [              <=>   ]  13.06M   135KB/s               
sv                      [               <=>  ]  13.09M   136KB/s               
v                       [                <=> ]  13.12M   139KB/s               
                        [                 <=>]  13.18M   142KB/s               
                  d     [                <=> ]  13.21M   139KB/s               
                 da     [               <=>  ]  13.25M   141KB/s               
                dat     [              <=>   ]  13.31M   141KB/s               
               data     [             <=>    ]  13.34M   140KB/s               
              data/     [            <=>     ]  13.37M   134KB/s               
             data/r     [           <=>      ]  13.43M   139KB/s               
            data/re     [          <=>       ]  13.46M   137KB/s               
           data/rec     [         <=>        ]  13.56M   141KB/s               
          data/rece     [        <=>         ]  13.65M   150KB/s               
         data/recen     [       <=>          ]  13.71M   154KB/s               
        data/recens     [      <=>           ]  13.81M   163KB/s               
       data/recense     [     <=>            ]  13.90M   174KB/s               
      data/recensem     [    <=>             ]  13.96M   170KB/s               
     data/recenseme     [   <=>              ]  14.06M   182KB/s               
    data/recensemen     [  <=>               ]  14.15M   187KB/s               
   data/recensement     [ <=>                ]  14.25M   195KB/s               
  data/recensement_     [<=>                 ]  14.31M   198KB/s               
 data/recensement_i     [ <=>                ]  14.40M   199KB/s               
data/recensement_in     [  <=>               ]  14.50M   208KB/s               
ata/recensement_ind     [   <=>              ]  14.56M   208KB/s               
ta/recensement_indi     [    <=>             ]  14.65M   214KB/s               
a/recensement_indiv     [     <=>            ]  14.75M   224KB/s               
/recensement_indivi     [      <=>           ]  14.84M   237KB/s               
recensement_individ     [       <=>          ]  14.90M   235KB/s               
ecensement_individu     [        <=>         ]  15.00M   252KB/s               
censement_individus     [         <=>        ]  15.09M   251KB/s               
ensement_individus.     [          <=>       ]  15.15M   251KB/s               
nsement_individus.c     [           <=>      ]  15.25M   250KB/s               
sement_individus.cs     [            <=>     ]  15.34M   254KB/s               
ement_individus.csv     [             <=>    ]  15.40M   243KB/s               
ment_individus.csv      [              <=>   ]  15.50M   244KB/s               
ent_individus.csv       [               <=>  ]  15.59M   248KB/s               
nt_individus.csv        [                <=> ]  15.68M   249KB/s               
t_individus.csv         [                 <=>]  15.75M   240KB/s               
_individus.csv          [                <=> ]  15.84M   250KB/s               
individus.csv           [               <=>  ]  15.93M   252KB/s               
ndividus.csv            [              <=>   ]  16.00M   254KB/s               
dividus.csv             [             <=>    ]  16.09M   256KB/s               
ividus.csv              [            <=>     ]  16.18M   257KB/s               
vidus.csv               [           <=>      ]  16.28M   265KB/s               
idus.csv                [          <=>       ]  16.34M   260KB/s               
dus.csv                 [         <=>        ]  16.43M   260KB/s               
us.csv                  [        <=>         ]  16.53M   259KB/s               
s.csv                   [       <=>          ]  16.59M   259KB/s               
.csv                    [      <=>           ]  16.68M   260KB/s               
csv                     [     <=>            ]  16.78M   261KB/s               
sv                      [    <=>             ]  16.84M   259KB/s               
v                       [   <=>              ]  16.93M   256KB/s               
                        [  <=>               ]  17.03M   256KB/s               
                  d     [ <=>                ]  17.12M   273KB/s               
data/recensement_in     [<=>                 ]  17.14M   276KB/s    in 89s     

2022-05-13 04:05:35 (198 KB/s) - ‘data/recensement_individus.csv’ saved [17971968]
!wget "https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C" -O data/recensement_logements.csv
--2022-05-13 04:05:35--  https://data.gouv.nc/explore/dataset/rp-2019-logements/download/?format=csv&timezone=Pacific/Noumea&lang=fr&use_labels_for_header=true&csv_separator=%2C
Resolving data.gouv.nc (data.gouv.nc)... 13.211.119.48, 13.55.171.246
Connecting to data.gouv.nc (data.gouv.nc)|13.211.119.48|:443... 
connected.
HTTP request sent, awaiting response... 
200 OK
Length: unspecified [application/csv]
Saving to: ‘data/recensement_logements.csv’


          data/rece     [<=>                 ]       0  --.-KB/s               
         data/recen     [ <=>                ] 100.67K   258KB/s               
        data/recens     [  <=>               ] 276.63K   330KB/s               
       data/recense     [   <=>              ] 356.61K   269KB/s               
      data/recensem     [    <=>             ] 468.59K   250KB/s               
     data/recenseme     [     <=>            ] 596.57K   250KB/s               
    data/recensemen     [      <=>           ] 708.55K   246KB/s               
   data/recensement     [       <=>          ] 796.55K   234KB/s               
  data/recensement_     [        <=>         ] 892.55K   236KB/s               
 data/recensement_l     [         <=>        ] 988.55K   237KB/s               
data/recensement_lo     [          <=>       ]   1.06M   239KB/s               
ata/recensement_log     [           <=>      ]   1.18M   245KB/s               
ta/recensement_loge     [            <=>     ]   1.28M   245KB/s               
a/recensement_logem     [             <=>    ]   1.37M   241KB/s               
/recensement_logeme     [              <=>   ]   1.46M   238KB/s               
recensement_logemen     [               <=>  ]   1.56M   240KB/s               
ecensement_logement     [                <=> ]   1.65M   241KB/s               
censement_logements     [                 <=>]   1.75M   244KB/s               
ensement_logements.     [                <=> ]   1.87M   247KB/s               
nsement_logements.c     [               <=>  ]   1.96M   239KB/s               
sement_logements.cs     [              <=>   ]   2.06M   241KB/s               
ement_logements.csv     [             <=>    ]   2.15M   245KB/s               
ment_logements.csv      [            <=>     ]   2.25M   245KB/s               
ent_logements.csv       [           <=>      ]   2.34M   248KB/s               
nt_logements.csv        [          <=>       ]   2.46M   260KB/s               
t_logements.csv         [         <=>        ]   2.56M   260KB/s               
_logements.csv          [        <=>         ]   2.65M   261KB/s               
logements.csv           [       <=>          ]   2.75M   258KB/s               
ogements.csv            [      <=>           ]   2.84M   262KB/s               
gements.csv             [     <=>            ]   2.93M   258KB/s               
ements.csv              [    <=>             ]   3.06M   265KB/s               
ments.csv               [   <=>              ]   3.15M   267KB/s               
ents.csv                [  <=>               ]   3.25M   271KB/s               
nts.csv                 [ <=>                ]   3.34M   270KB/s               
ts.csv                  [<=>                 ]   3.43M   269KB/s               
s.csv                   [ <=>                ]   3.53M   268KB/s               
.csv                    [  <=>               ]   3.65M   266KB/s               
csv                     [   <=>              ]   3.75M   266KB/s               
sv                      [    <=>             ]   3.84M   265KB/s               
v                       [     <=>            ]   3.93M   267KB/s               
                        [      <=>           ]   4.03M   265KB/s               
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2022-05-13 04:06:19 (259 KB/s) - ‘data/recensement_logements.csv’ saved [11293731]
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv("data/recensement_individus.csv")
df['COUPLE'] = df['COUPLE'].astype("category")
df['COUPLE'] = df['COUPLE'].cat.rename_categories({1: 'Vit en couple', 2: 'ne vit pas en couple'})

df['CS24'] = df['CS24'].astype("category")
df['CS24'] = df['CS24'].cat.rename_categories({10: 'Agriculteurs exploitants', 21: 'Artisans', 22: 'Commerçants et assimilés', 23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 32: 'Cadres de la fonction publique, professions intellectuelles et artistiques',
                                               36: 'Cadres d\'entreprise', 41: 'Professions intermédiaires de l\'enseignement, de la santé, de la fonction publique et assimilés',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 51: 'Employés de la fonction publique',
                                               54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce', 56: 'Personnels des services directs aux particuliers',
                                               61: 'Ouvriers qualifiés', 66: 'Ouvriers non qualifiés', 69: 'Ouvriers agricoles'})

df['CS42'] = df['CS42'].astype("category")
df['CS42'] = df['CS42'].cat.rename_categories({11: 'Agriculteurs sur petites exploitations', 12: 'Agriculteurs sur moyennes exploitations', 13: 'Agriculteurs sur grandes exploitations',
                                               21: 'Artisans', 22: 'Commerçants et assimilés' ,23: 'Chefs d\'entreprise de 10 salariés ou plus',
                                               31: 'Professions libérales et assimilés', 33: 'Cadres de la fonction publique', 34: 'Professeurs, professions scientifiques',
                                               35: 'Professions de l\'information, des arts et des spectacles', 37: 'Cadres administratifs et commerciaux d\'entreprise',
                                               38: 'Ingénieurs et cadres techniques d\'entreprise', 42: 'Professeurs des écoles, instituteurs et assimilés',
                                               43: 'Professions intermédiaires de la santé et du travail social',
                                               44: 'Clergé, religieux', 45: 'Professions intermédiaires administratives de la fonction publique',
                                               46: 'Professions intermédiaires administratives et commerciales des entreprises', 47: 'Techniciens',
                                               48: 'Contremaîtres, agents de maîtrise', 52: 'Employés civils et agents de service de la fonction publique',
                                               53: 'Policiers et militaires', 54: 'Employés administratifs d\'entreprise', 55: 'Employés de commerce',
                                               56: 'Personnels des services directs aux particuliers', 62: 'Ouvriers qualifiés de type industriel',
                                               63: 'Ouvriers qualifiés de type artisanal', 64: 'Chauffeurs', 65: 'Ouvriers qualifiés de la manutention, du magasinage et du transport',
                                               67: 'Ouvriers non qualifiés de type industriel', 68: 'Ouvriers non qualifiés de type artisanal',
                                               69: 'Ouvriers agricoles'})


df['CS8'] = df['CS8'].astype("category")
df['CS8'] = df['CS8'].cat.rename_categories({1: 'Agriculteurs exploitants', 2: 'Artisans, commerçants et chefs d\'entreprise',
                                             3: 'Cadres et professions intellectuelles supérieures', 4 : 'Professions Intermédiaires',
                                             5: 'Employés', 6: 'Ouvriers'})


df['CSSAL'] = df['CSSAL'].astype("category")
df['CSSAL'] = df['CSSAL'].cat.rename_categories({1: 'Manœuvre, ouvrier spécialisé', 2: 'Ouvrier qualifié ou hautement qualifié, technicien d’atelier',
                                                 3: 'Technicien (non cadre)', 4 : 'Agent de catégorie B de la fonction publique',
                                                 5: 'Agent de maîtrise, maîtrise administrative ou commerciale, VRP', 6: 'Agent de catégorie A de la fonction publique',
                                                 7: 'Ingénieur, cadre d’entreprise', 8: 'Agent de catégorie C ou D de la fonction publique',
                                                 9: 'Employé (par exemple : de bureau, de commerce, de la restauration, de maison)'})

EMPL_labels = {3: 'Artisan, commerçant, industriel, travailleur indépendant', 4: 'Stagiaire rémunéré, apprenti sous contrat',
                5: 'Salarié du secteur privé à durée déterminée', 6: 'Salarié du secteur privé à durée indéterminée',
                7 : 'Salarié du secteur public à durée déterminée', 8: 'Salarié du secteur public à durée indéterminé', }
df['EMPL'] = df['EMPL'].astype("category")


df['DIPL'] = df['DIPL'].astype("category")
diplomes_libelles = {1: 'Pas de scolarisation', 2: 'Aucun diplôme mais scolarisation jusqu’en primaire', 3: 'Aucun diplôme mais scolarisation jusqu’au collège', 
            4: 'Aucun diplôme mais scolarisation au-delà du collège', 11: 'CEP' , 12: 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 13: 'CAP, BEP ou diplôme de niveau équivalent',
            14: 'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            15: 'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            16: 'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            17: 'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            18: 'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            19: 'Doctorat de recherche (hors santé)'}
#df['DIPL'] = df['DIPL'].cat.rename_categories(diplomes_libelles)




#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })

#df['CS8'] = df['CS8'].astype("category")
#df['CS8'] = df['CS8'].cat.rename_categories({ : '',: '', : '',: '', : '', })


df.head()
ID IDLOG AGEA AGER ANNINS APE CNAT COUPLE CPAYSN CPAYSRA ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
0 200296 85580.0 7 7 NaN NaN NaN NaN NaN NaN ... NaN NaN 6 NaN NaN NaN NaN 2 NaN 3.0
1 200301 48282.0 46 46 NaN 2410Z NaN Vit en couple NaN NaN ... 3.0 NaN 2 1.0 1.0 NaN 4.0 2 1.0 3.0
2 200307 477.0 15 15 NaN NaN NaN ne vit pas en couple NaN NaN ... NaN NaN 6 3.0 NaN 2.0 5.0 2 NaN 3.0
3 200315 94323.0 40 40 NaN 8411Z NaN Vit en couple NaN NaN ... 3.0 NaN 3 1.0 1.0 NaN 4.0 2 1.0 3.0
4 200318 107640.0 68 68 1972.0 NaN NaN Vit en couple NaN NaN ... NaN 1.0 1 5.0 NaN 1.0 4.0 2 NaN 5.0

5 rows × 43 columns

df.describe()
ID IDLOG AGEA AGER ANNINS CNAT CPAYSN CPAYSRA EXER GAD ... STAT STATANT STM TACT TP TRAANT TRANS TYP TYPEMPL TYPMENR
count 203144.000000 199929.000000 203144.000000 203144.000000 44082.000000 2412.000000 3137.00000 131.000000 89638.000000 203144.000000 ... 89155.000000 41032.000000 203144.000000 162368.000000 89638.000000 61332.000000 162852.000000 203144.000000 68942.000000 199844.000000
mean 135704.624434 54598.597797 35.574174 35.283617 1999.589651 432.129353 476.13803 454.458015 1.081428 3.085555 ... 2.755224 1.116494 3.879258 2.618632 1.114003 1.281729 3.701183 2.016245 1.252894 3.270726
std 78350.449683 31455.117174 21.826895 21.821489 17.898269 268.332958 76.04784 124.236639 0.328495 2.186296 ... 0.810840 0.379688 2.129446 1.953191 0.317817 0.449846 1.125026 0.126415 0.717101 1.215611
min 1.000000 2.000000 0.000000 0.000000 1927.000000 103.000000 127.00000 132.000000 1.000000 0.000000 ... 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 1.000000 2.000000 1.000000 1.000000
25% 67790.500000 27344.000000 17.000000 17.000000 1988.000000 219.000000 501.00000 501.000000 1.000000 1.000000 ... 3.000000 1.000000 1.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
50% 135765.000000 54590.000000 35.000000 34.000000 2006.000000 416.000000 514.00000 501.000000 1.000000 3.000000 ... 3.000000 1.000000 4.000000 1.000000 1.000000 1.000000 4.000000 2.000000 1.000000 3.000000
75% 203519.250000 81991.000000 52.000000 51.000000 2015.000000 514.000000 514.00000 501.000000 1.000000 5.000000 ... 3.000000 1.000000 6.000000 5.000000 1.000000 2.000000 4.000000 2.000000 1.000000 4.000000
max 271406.000000 109025.000000 104.000000 104.000000 2019.000000 999.000000 514.00000 514.000000 3.000000 9.000000 ... 9.000000 3.000000 6.000000 7.000000 2.000000 2.000000 5.000000 3.000000 9.000000 5.000000

8 rows × 30 columns

from dataprep.eda import plot
plot(df)
DataPrep.EDA Report
Dataset Statistics
Number of Variables 43
Number of Rows 203144
Missing Cells 3.5524e+06
Missing Cells (%) 40.7%
Duplicate Rows 0
Duplicate Rows (%) 0.0%
Total Size in Memory 103.8 MB
Average Row Size in Memory 535.9 B
Variable Types
  • Numerical: 9
  • Categorical: 34
Dataset Insights
ID is uniformly distributed Uniform
AGEA and AGER have similar distributions Similar Distribution
CNAT and CPAYSN have similar distributions Similar Distribution
IDLOG has 3215 (1.58%) missing values Missing
ANNINS has 159062 (78.3%) missing values Missing
APE has 156869 (77.22%) missing values Missing
CNAT has 200732 (98.81%) missing values Missing
COUPLE has 40776 (20.07%) missing values Missing
CPAYSN has 200007 (98.46%) missing values Missing
CPAYSRA has 203013 (99.94%) missing values Missing
Dataset Insights
CS24 has 128604 (63.31%) missing values Missing
CS42 has 128604 (63.31%) missing values Missing
CS8 has 128604 (63.31%) missing values Missing
CSSAL has 140423 (69.12%) missing values Missing
DIPL has 40776 (20.07%) missing values Missing
EMPL has 113506 (55.87%) missing values Missing
EXER has 113506 (55.87%) missing values Missing
IRA has 13748 (6.77%) missing values Missing
MINE has 199871 (98.39%) missing values Missing
PROVRA has 13748 (6.77%) missing values Missing
Dataset Insights
PROVTRA has 113506 (55.87%) missing values Missing
RECH has 144665 (71.21%) missing values Missing
SAL has 191662 (94.35%) missing values Missing
SCOL has 26951 (13.27%) missing values Missing
SECT10 has 113506 (55.87%) missing values Missing
SECT21 has 113506 (55.87%) missing values Missing
SECT5 has 113506 (55.87%) missing values Missing
STAT has 113989 (56.11%) missing values Missing
STATANT has 162112 (79.8%) missing values Missing
TACT has 40776 (20.07%) missing values Missing
Dataset Insights
TP has 113506 (55.87%) missing values Missing
TRAANT has 141812 (69.81%) missing values Missing
TRANS has 40292 (19.83%) missing values Missing
TYPEMPL has 134202 (66.06%) missing values Missing
TYPMENR has 3300 (1.62%) missing values Missing
ANNINS is skewed Skewed
CNAT is skewed Skewed
CPAYSN is skewed Skewed
GAD is skewed Skewed
APE has a high cardinality: 369 distinct values High Cardinality
Dataset Insights
MINE has constant value "1.0" Constant
PROV has constant value "Sud" Constant
APE has constant length 5 Constant Length
CPAYSRA has constant length 5 Constant Length
EMPL has constant length 3 Constant Length
EXER has constant length 3 Constant Length
GENRE has constant length 1 Constant Length
ILN has constant length 1 Constant Length
IRA has constant length 3 Constant Length
MINE has constant length 3 Constant Length
Dataset Insights
NAT has constant length 1 Constant Length
PROV has constant length 3 Constant Length
RECH has constant length 3 Constant Length
SAL has constant length 3 Constant Length
SCOL has constant length 3 Constant Length
SECT10 has constant length 2 Constant Length
SECT21 has constant length 1 Constant Length
SECT5 has constant length 3 Constant Length
STAT has constant length 3 Constant Length
STATANT has constant length 3 Constant Length
Dataset Insights
STM has constant length 1 Constant Length
TACT has constant length 3 Constant Length
TP has constant length 3 Constant Length
TRAANT has constant length 3 Constant Length
TRANS has constant length 3 Constant Length
TYP has constant length 1 Constant Length
TYPEMPL has constant length 3 Constant Length
TYPMENR has constant length 3 Constant Length
GAD has 28705 (14.13%) zeros Zeros
GAQ has 13748 (6.77%) zeros Zeros
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Number of plots per page:

#df2 = df[['ID', 'DIPL', 'EMPL']]
#df2 = df2.fillna(0)
#df2.head()
#df2 = df2.pivot_table('DIPL', 'EMPL', 'ID', aggfunc="sum")

#f, ax = plt.subplots(figsize=(9, 6))
#sns.heatmap(df2, annot=True, linewidths=.5, ax=ax)
df_age = df[['AGER', 'GENRE']]
df_homme = df_age.loc[df_age['GENRE'] == 1].groupby('AGER').sum()
df_homme['AGER'] = df_homme.index
print(df_homme.shape)
print(df_homme.loc[df_homme['AGER'] == 40])
df_homme['GENRE'] = 0-df_homme['GENRE']
df_homme = df_homme.rename(columns={'GENRE': 'homme'})


df_femme = df_age.loc[df_age['GENRE'] == 2].groupby('AGER').sum()
df_femme = df_femme.rename(columns={'GENRE': 'femme'})
df_femme['AGER'] = df_femme.index
print(df_femme.shape)
df_femme.loc[df_femme['AGER'] == 40]
(101, 2)
      GENRE  AGER
AGER             
40     1349    40
(105, 2)
femme AGER
AGER
40 3094 40

Pyramide des ages

sns.set(font_scale = 2)
df4 = pd.concat([df_homme, df_femme], axis=1).iloc[::-1]
figure = plt.figure(figsize=(50, 50))

bar_plot = sns.barplot(x='homme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot = sns.barplot(x='femme', y=df4.index, data=df4, order=df4.index, lw=0, orient='horizontal')
bar_plot.set(ylabel="Age", xlabel="Nombre de personnes", title = "Pyramide des âges")
plt.plot([0,0], [0,105], linewidth=2)
#sns;barplot(data=df_age, x=)
[<matplotlib.lines.Line2D at 0x7fb34caf38e0>]
_images/recensement_11_1.png

Relation entre niveau de diplome, type d’emploi et catégorie socioprofessionnelle

df_metier = df[['DIPL', 'EMPL', 'CS8']].dropna()
sns.set(font_scale = 2)
fig, ax = plt.subplots(figsize=(50, 30))
df_counts = df_metier.groupby(['DIPL', 'EMPL']).size().reset_index()
df_counts.columns.values[df_counts.columns == 0] = 'count'
scale = 500*df_counts['count'].size
size = df_counts['count']/df_counts['count'].sum()*scale
#size = size.astype(float)

#sns.stripplot(x='DIPL', y='EMPL', hue='CS8', data=df_metier, ax=ax) #, size=size, sizes=(10,500)
dipl_id = [1, 2, 3, 4, 11, 12, 13, 14, 15, 16, 17, 18, 19]
dipl_lbl = ['Pas de scolarisation', 'Aucun diplôme mais scolarisation jusqu’en primaire', 'Aucun diplôme mais scolarisation jusqu’au collège', 
            'Aucun diplôme mais scolarisation au-delà du collège', 'CEP' , 'BEPC, brevet élémentaire, brevet des collèges, DNB' , 'CAP, BEP ou diplôme de niveau équivalent',
            'Bac général ou technologique, brevet supérieur, capacité en droit, DAEU, ESEU',
            'Bac professionnel, brevet professionnel de technicien ou d’enseignement, diplôme équivalent',
            'BTS, DUT, Deug, Deust, diplôme de santé ou du social niveau bac + 2, diplôme équivalent',
            'Licence, Licence pro, maîtrise, diplôme équivalent de niveau bac + 3 ou bac + 4',
            'Master, DEA, diplôme grande école niveau bac + 5, doctorat de santé',
            'Doctorat de recherche (hors santé)']
#plt.xticks(dipl_id, dipl_lbl, rotation=45, )

empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
            'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
            'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']


from sklearn.preprocessing import OrdinalEncoder
import numpy as np
ord_enc = OrdinalEncoder()
enc_df = pd.DataFrame(ord_enc.fit_transform(df_metier), columns=list(df_metier.columns))

xnoise, ynoise = np.random.random(len(df_metier))/2, np.random.random(len(df_metier))/2 

sns.scatterplot(enc_df["DIPL"]+xnoise, enc_df["EMPL"]+ynoise, alpha=0.5, hue=enc_df['CS8'], palette="hls")

plt.yticks(np.arange(0.25, len(empl_lbl)+0.25, 1), empl_lbl)

xrange = np.arange(0.25, len(dipl_lbl)+0.25, 1)
plt.xticks(xrange, dipl_lbl, rotation=90)

plt.legend(title='Categories socioprofessionnelles', loc='lower left', labels=['Agriculteurs exploitants', 'Artisans, commerçants et chefs d\'entreprise',
                                                         'Cadres et professions intellectuelles supérieures', 'Professions Intermédiaires',
                                                         'Employés', 'Ouvriers'])
---------------------------------------------------------------------------
ModuleNotFoundError                       Traceback (most recent call last)
Input In [11], in <cell line: 27>()
     20 #plt.xticks(dipl_id, dipl_lbl, rotation=45, )
     22 empl_lbl = ['Artisan, commerçant, industriel, travailleur indépendant', 'Stagiaire rémunéré, apprenti sous contrat',
     23             'Salarié du secteur privé à durée déterminée', 'Salarié du secteur privé à durée indéterminée',
     24             'Salarié du secteur public à durée déterminée', 'Salarié du secteur public à durée indéterminé']
---> 27 from sklearn.preprocessing import OrdinalEncoder
     28 import numpy as np
     29 ord_enc = OrdinalEncoder()

ModuleNotFoundError: No module named 'sklearn'
_images/recensement_13_1.png